What happened after the second AI winter?
My reading of AI development (somewhat simplified here) is that the availability of large data sets, increased computing power, and the introduction of new machine learning algorithms (which require large data sets and massive computing power) contributed to the resurgence of AI.

However, as witnessed on this site, there has been a paradigm shift within the field: while previous approaches to AI were largely symbolic, with a bit of connectionism thrown in, the current AI mainstream is purely based on statistical models and machine learning.

Massive (distributed) computing power on its own is not sufficient, as there was a bottleneck with domain modelling/knowledge acquisition in traditional symbolic approaches. New algorithms (basically further developments of neural networks) on their own would also not be sufficient without massive amounts of training data. Only the combination of all three elements enabled the AI resurgence.